Addressing　the　issue　of　electrical　fires　caused　by　DC　series　arc　fault　in　long-serving　photovoltaic　systems　due　to　line　aging　and　poor　contact,　this　paper　proposes　a　fault　detection　method　that　combines　multi-feature　fusion　fault　criteria　with　machine　learning　algorithms.　Initially,　an　arc　generator　is　designed　and　integrated　into　the　photovoltaic　system　to　produce　DC　arc　fault　waveforms,　with　arc　data　collected　using　sensors　and　oscilloscopes.　Subsequently,　arc　features　are　extracted　from　various　domains　and　feature　selection　is　performed　using　the　random　forest　algorithm　to　eliminate　redundant　features,　forming　feature　criteria.　Finally,　arc　fault　detection　is　carried　out　using　a　support　vector　machine　optimized　by　particle　swarm　optimization.　Experimental　results　show　that　this　method　can　accurately　identify　DC　series　arc　fault　in　photovoltaic　systems　and　has　a　certain　degree　of　anti-interference　capability.　©　Beijing　Paike　Culture　Commu.　Co.,　Ltd.　2025.